AI Code Review Skill

Human code fails where the human got tired or didn't know; AI code fails where plausibility diverged from correctness — and it fails fluently, with confident naming, clean formatting, and tests that pass without testing anything. Reviewing it with human-code instincts ("looks careful, probably is careful") is how the new bug class ships. This skill reviews for the failure modes that are characteristically AI.

What This Skill Produces

• A review of the change organised by AI-characteristic risk, each finding with file/line and severity
• Verification steps the reviewer must actually run (not read) per risk class
• A team checklist for AI-authored PRs, calibrated to this codebase

Required Inputs

Ask for (if not already provided):

• The diff or PR (or the files changed)
• Provenance honestly: fully agent-written, human-piloted, or mixed — and whether the author reviewed it themselves before requesting review
• The codebase context: existing conventions/utilities the AI may not have known, and what the change claims to do
• Test infrastructure: what CI actually runs (the AI may have written tests CI never executes)

The AI-Characteristic Failure Modes